Creating and Collaborating: Students’ and Tutors’ Perceptions of an Online Group Project
Bibliographic record
Abstract
Although collaboration skills are highly valued by employers, convincing students that collaborative learning activities are worthwhile, and ensuring that the experience is both useful and enjoyable, are significant challenges for educators. This paper addresses these challenges by exploring students’ and tutors’ experiences of a group project where part-time distance learners collaborate online to create a website. Focus groups were conducted with students who had recently completed the project, and discussion forums were used to gather feedback from tutors who supported students and marked their group work. The research showed that students’ attitudes towards the group project on completion were generally favourable. Findings highlighted key aspects for successful online group projects and for motivating students to participate fully. These included: the design of authentic tasks, with skills development relevant to the workplace; careful attention to how the group work is assessed; and enabling students to develop websites they could be proud of. Frustrations for students were associated with the lack of engagement of fellow students and with limitations of the tool provided for building the website. Tutors found marking the work a time-consuming and complex process. Tutors were also unconvinced of the value and fairness of assessing students partly on a group, as opposed to an individual, basis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".